Prof. Shaodan Ma of University of Macau (UM)

A new architecture "Reconfigurable Distributed Antennas and Reflecting Surface (RDARS) " will be introduced for future 6G wireless communications in this talk. Specifically, RDARS is a flexible combination of distributed antenna system (DAS) and reconfigurable intelligent surface (RIS). It inherits the low-cost and low-energy-consumption benefits of fully-passive RISs by default configuring all the elements as passive to perform the reflection mode. On the other hand, based on the design of the additional direct-through state, any element of the RDARS can be dynamically programmed to connect with the base station (BS) via fibers/wires and perform the connected mode as remote distributed antennas of the BS to transmit/receive signals. As such, this novel architecture exploits the benefits from both RIS and DAS with a controllable trade-off between the "reflection gain" and the "distribution gain" achieved via RDARS at the BS. Moreover, additional "selection gain" can be achieved from the reconfigurability of the operation modes for each element. Outage probability and ergodic achievable rate under maximum ratio combining (MRC) scheme at BS are analyzed in closed-forms to characterize the system behavior of the RDARS-aided system. Experimental results on a prototype of RDARS with 256 elements are also shown to demonstrate the superiority of the proposed RDARS. Particularly, the RDARS aided system with only one element operating in connected mode can achieve an additional 21% and 170% throughput improvement over DAS and RIS-aided systems, respectively. This confirms the high effectiveness and potential of the proposed RDARS for future 6G wireless systems. Finally, the RDARS-enabled new paradigm of integrated sensing and communications is briefly introduced with a practical demo to showcase the high flexibility and potential of RDARS.

Biography:

Dr. Shaodan Ma is a Professor at the Department of Electrical and Computer Engineering, and Associate Director of the State Key Laboratory of Internet of Things for Smart City, University of Macau (UM). She received her Ph.D. degree in electrical and electronic engineering from The University of Hong Kong in 2006, and joined UM in 2011. Her research interests include array signal processing, transceiver design, localization, integrated sensing and communication, mmWave/THz communications, massive MIMO, and machine learning for communications. She was a symposium co-chair for various conferences including IEEE VTC2024-Spring, IEEE ICC 2021, 2019 & 2016, IEEE GLOBECOM 2016, IEEE/CIC ICCC 2019, etc. She is an IEEE ComSoc Distinguished Lecturer in 2024-2025 and has served as an Editor for IEEE Wireless Communications (2024-present), IEEE Communications Letters (2023), Journal of Communications and Information Networks (2021-present), IEEE Transactions on Wireless Communications (2018-2023), IEEE Transactions on Communications (2018-2023), and IEEE Wireless Communications Letters (2017-2022). She was the awardee of Macao Science and Technology Awards (3rd prize, Natural Science Award) in 2022.

Prof. Ying Cui of The Hong Kong University of Science and Technology (Guangzhou)

Existing optimization and deep learning approaches for channel state information (CSI)-adaptive beamforming and power control cannot achieve satisfactory performance and computation time trade-offs for large-scale interference networks with arbitrary device locations. In this talk, we present a novel approach for (CSI)-adaptive beamforming and power control in an OFDM-MIMO-based interference network, combining optimization and learning techniques. First, we propose a parallel iterative algorithm to obtain stationary points of the challenging large-scale non-convex optimization problem. This algorithm has parallel and closed-form per-iteration updates, significantly reducing the computation time. Next, we propose a data and model-driven deep learning method that optimally selects good initial points and effectively unrolls the proposed parallel algorithm into neural networks with the algorithm parameters as tunable parameters. This method further reduces the computation time while maintaining appealing performance based on training over vast samples. Numerical results demonstrate the proposed approach’s superior advantages over the state of the art.

Biography:

Ying Cui received her B.Eng degree in Electronic and Information Engineering from Xi’an Jiao Tong University, China, in 2007 and her Ph.D. from the Hong Kong University of Science and Technology, Hong Kong SAR, China, in 2012. She held visiting positions at Yale University, US, in 2011 and Macquarie University, Australia, in 2012. From June 2012 to June 2013, she was a postdoctoral research associate at Northeastern University, US. From July 2013 to December 2014, she was a postdoctoral research associate at the Massachusetts Institute of Technology, US. From January 2015 to July 2022, she was an associate professor at Shanghai Jiao Tong University, China. Since August 2022, she has been an associate professor with the IoT Thrust at The Hong Kong University of Science and Technology (Guangzhou), China. She has published over 90 papers in prestigious IEEE journals and 85 papers in leading IEEE conferences. She was selected to the National Young Talent Program in 2014 and the World's Top 2% Scientists for the Years 2020-2025. She received Best Paper Awards from IEEE ICC 2015 and IEEE GLOBECOM 2021. She serves as an Editor for the IEEE Transactions on Wireless Communications (2018-2024) and the IEEE Transactions on Communications (2025-now).

Prof. Junting Chen of The Chinese University of Hong Kong, Shenzhen

How to enhance the intelligence for wireless communication networks? One promising direction is to fuse more environmental information to the network, such as building radio maps, a data model that describes the wireless communication quality between any transmitter and receiver location pair. The technology has been successfully used for network planning, spectrum management, and fingerprint localization for over 20 years. However, conventional radio map techniques were limited to power spectrum maps and require precise location labels for construction and application. In this talk, we attempt the following two questions: Can we reconstruct and update a radio map from pilot sequences without precise location labels, and can radio map help reduce pilots for CSI tracking.

We will start from an indoor scenario, where we develop a region-based radio map from received signal strength (RSS) measurements without location labels. A signal subspace model with a sequential prior is constructed for the RSS data, and an integrated segmentation and clustering algorithm is developed, which is shown to find the globally optimal solution in a special case. We demonstrate a reduction of region localization error by roughly 50% compared to existing schemes. In the outdoor scenario, we study the problem of radio-map-embedded CSI tracking and radio map construction without the assumptions of stationary CSI statistics and precise location labels. Using radio maps as the prior information, we develop a radio-map-embedded switching Kalman filter (SKF) framework that jointly tracks the location and the CSI with adaptive beamforming for sparse CSI observations under reduced pilots. For radio map construction without precise location labels, the location sequence and the channel covariance matrices are jointly estimated based on a Hidden Markov Model (HMM). An unbiased estimator on the channel covariance matrix is found. Numerical results on ray-traced MIMO channel datasets demonstrate that using 1 pilot in every 10 milliseconds, an average of over 80% of capacity over that of perfect CSI can be achieved for a user moving at 36 km/h at a 20 dB signal-to-noise ratio (SNR). Furthermore, the proposed radio-map-embedded CSI model can reduce the localization error from 30 meters from the prior to 6 meters for radio map construction.

Biography:

Junting Chen (S’11–M’16) received the Ph.D. degree in electronic and computer engineering from The Hong Kong University of Science and Technology (HKUST), Hong Kong SAR China, in 2015, and the B.Sc. degree in electronic engineering from Nanjing University, Nanjing, China, in 2009. He is currently an Assistant Professor with the School of Science and Engineering, the Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), Guangdong, China. Prior to joining CUHK-Shenzhen, he was a Postdoctoral Research Associate with the Ming Hsieh Department of Electrical Engineering, University of Southern California (USC), Los Angeles, CA, USA, from 2016–2018, and with the Communication Systems Department of EURECOM, Sophia-Antipolis, France, from 2015–2016. From 2014–2015, he was a visiting student with the Wireless Information and Network Sciences Laboratory at MIT, Cambridge, MA, USA. He served as a patent consultant for Nokia in 2018.

Dr. Chen works in the field of signal processing, optimization, and machine learning for wireless communications and localization. He focuses on applications in 5G/6G cellular communications and localization, underwater acoustic communication and localization, low-altitude air-to-ground integrated communications, massive MIMO, and radio maps. As a young scholar, Dr. Chen has published near 100 papers in leading journals and conference proceedings, and has contributed to over 10 patents. He was a recipient of several province-level and national-level talent awards. He won the Charles Kao Best Paper Award in WOCC 2022. He currently serves as an editor for IEEE Transactions on Wireless Communications. He was recognized as the Top 2% Scientist in 2025.